AbstractBackgroundX‐ray digital subtraction angiography (DSA) is the imaging modality for peri‐procedural guidance and treatment evaluation in (neuro‐) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied.PurposeTo comparatively study the independence of deconvolution‐based perfusion DSA with respect to varying injection protocols, as well as its sensitivity to alterations in brain conditions.MethodsWe developed a deconvolution‐based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV), cerebral blood flow (CBF), time to maximum (Tmax), and mean transit time (MTT) and applied it to DSA sequences obtained from two swine models. We also extracted the time intensity curve (TIC)‐derived parameters, that is, area under the curve (AUC), peak concentration of the curve, and the time to peak (TTP) from these sequences. Deconvolution‐based parameters were quantitatively compared to TIC‐derived parameters in terms of consistency upon variations in injection profile and time resolution of DSA, as well as sensitivity to alterations of cerebral condition.ResultsComparing to TIC‐derived parameters, the standard deviation (SD) of deconvolution‐based parameters (normalized with respect to the mean) are two to five times smaller, indicating that they are more consistent across different injection protocols and time resolutions. Upon ischemic stroke induced in a swine model, the sensitivities of deconvolution‐based parameters are equal to, if not higher than, those of TIC‐derived parameters.ConclusionsIn comparison to TIC‐derived parameters, deconvolution‐based perfusion imaging in DSA shows significantly higher quantitative reliability against variations in injection protocols across different time resolutions, and is sensitive to alterations in cerebral hemodynamics. The quantitative nature of perfusion angiography may allow for objective treatment assessment in neurovascular interventions.